Interpret Amazon Brand Analytics data and take action. Analyze Search Frequency Rank (SFR), click share, conversion share, market basket analysis, and repeat purchase behavior patterns to optimize your Amazon strategy.
Supported platforms: Amazon、Shopify、WooCommerce、Walmart、TikTok Shop、Etsy、eBay、BigCommerce。
How to use
帮我分析我的亚马逊品牌分析数据。我的热门搜索词 SFR #5,200，点击份额15%但转化份额只有8%。我该怎么做？
Features
- Search Frequency Rank (SFR) trend interpretation
- Click share and conversion share analysis
- Market basket analysis (frequently bought together insights)
- Repeat purchase behavior patterns
- Demographic data interpretation
- Search term performance optimization recommendations
How it works
Step 1: Gather information from the user message — product, platform, current situation, and goals.
Step 2: Ask all follow-up questions at once in multiple-choice format. Allow shorthand answers (e.g., "1b 2c 3a").
Step 3: Use the frameworks and methodology below to conduct research and analysis.
Search Frequency Rank (SFR) analysis framework
- SFR interpretation: The smaller the number = the higher the search frequency. SFR #1 = the most searched term in the market. The core value lies in detecting trend changes — when SFR moves from 5,000 to 2,000, demand is heating up rapidly.
- Trend detection: Compare SFR across different periods. An upward SFR trend = demand growth or seasonal change. A downward SFR = waning interest.
- Relative vs absolute: SFR is a relative ranking, not an absolute volume. When overall search volume surges (e.g., Prime Day), SFR changes may not reflect real changes in search interest.
Click share vs conversion share diagnostic model
- Click share > conversion share: Consumers show initial interest but drop off on the detail page. Root causes: listing quality, price, weak reviews, or shipping options.
- Conversion share > click share: High conversion efficiency. Consider increasing ad spend to capture more clicks.
- Gap analysis: Calculate the difference (e.g., click share 15% conversion share 8% = 7% leakage). The larger the gap = the more urgent the listing optimization.
Market basket analysis framework
- Cross-sell opportunities: Identify products that are frequently bought together. Consider bundling strategies or running targeted ads on complementary products.
- Competitor intelligence: Analyze which competitors appear in the same shopping basket = understand substitution patterns.
- Association strength: The higher the co-purchase frequency = the stronger the consumer behavior link. Prioritize high-frequency associations.
Repeat purchase behavior model
- High repeat purchase rate: Products with a good user experience and consumable demand (skincare, food). Suitable for subscriptions and Save to lock in long-term revenue.
- Low repeat purchase rate: May indicate quality issues or a one-time-purchase product category. Compensate by expanding the product line to achieve higher customer lifetime value.
Data sources
- The user exports a CSV from the Seller Central Brand Analytics dashboard.
- Required: SFR data, click share, conversion share (minimum requirements).
- Optional: market basket data, repeat purchase data, and multi-period data for trend analysis.
- No direct API connection to Seller Central.
Limitations
- Brand Analytics is available only to Brand Registry members. Data has a delay of about 72 hours.
- SFR is a relative ranking — it cannot be directly compared across different overall search volume periods.
- Click/conversion share shows only the top 3 brands. Lower-ranked brands need to be estimated.
- Market basket shows only the highest-frequency products — it may miss low-frequency, high-margin opportunities.
- Cannot distinguish the reasons for customer churn (price, reviews, product issues) — additional data is needed for inference.
- Data formats may differ across marketplaces. Each marketplace needs to be analyzed independently.
Step 4: Provide structured, actionable output containing specific recommendations rather than vague guidance.
Output Format
- Give a summary of findings first
- Include specific data points and benchmarks (if any)
- Provide prioritized action items
- Estimates based on incomplete data must be marked with ⚠️
- End with specific next steps